2016
Lynda
Barton Poulson
04:41:18
English

All data science begins with good data. Data mining is a framework for collecting, searching, and filtering raw data in a systematic matter, ensuring you have clean data from the start. It also helps you parse large data sets, and get at the most meaningful, useful information. This course, Data Science Foundations: Data Mining, is designed to provide a solid point of entry to all the tools, techniques, and tactical thinking behind data mining.
Barton Poulson covers data sources and types, the languages and software used in data mining (including R and Python), and specific task-based lessons that help you practice the most common data-mining techniques: text mining, data clustering, association analysis, and more. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining.

001 Welcome.mp4
002 Who should watch this course.mp4
003 Exercise files.mp4
004 Data mining prerequisites.mp4
005 Algorithm prerequisites.mp4
006 Software prerequisites.mp4
007 Goals of data reduction.mp4
008 Data for data reduction.mp4
009 Data reduction in R.mp4
010 Data reduction in Python.mp4
011 Data reduction in Orange.mp4
012 Data reduction in RapidMiner.mp4
013 Clustering goals.mp4
014 Clustering data.mp4
015 Clustering in R.mp4
016 Clustering in Python.mp4
017 Clustering in BigML.mp4
018 Clustering in Orange.mp4
019 Classification goals.mp4
020 Classification data.mp4
021 Classification in R.mp4
022 Classification in Python.mp4
023 Classification in RapidMiner.mp4
024 Classification in KNIME.mp4
025 Anomaly detection goals.mp4
026 Anomaly detection data.mp4
027 Anomaly detection in R.mp4
028 Anomaly detection in Python.mp4
029 Anomaly detection in BigML.mp4
030 Anomaly detection in RapidMiner.mp4
031 Association analysis goals.mp4
032 Association analysis data.mp4
033 Association analysis in R.mp4
034 Association analysis in Python.mp4
035 Association analysis in Orange.mp4
036 Association analysis in KNIME.mp4
037 Regression analysis goals.mp4
038 Regression analysis data.mp4
039 Regression analysis in R.mp4
040 Regression analysis in Python.mp4
041 Regression analysis in KNIME.mp4
042 Regression analysis in RapidMiner.mp4
043 Sequence mining goals.mp4
044 Sequence mining algorithms.mp4
045 Sequence mining in R.mp4
046 Sequence mining in Python.mp4
047 Sequence mining in BigML - Part 1.mp4
048 Sequence mining in BigML - Part 2.mp4
049 Text mining goals.mp4
050 Text mining algorithms.mp4
051 Text mining in R.mp4
052 Text mining in Python.mp4
053 Text mining in RapidMiner.mp4
054 Next steps.mp4
Ex_Files_DSF_DataMining.zip

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